One of the major problems driving current research in statistical machine learning is the search for ways to exploit highly-structured models that are both expressive and tractable. Nonparametric Bayesian methodology provides significant leverage on this problem. In the nonparametric Bayesian framework, the prior distribution is not a fixed parametric form, but is rather a general stochastic process—-a distribution over a possibly uncountably infinite number of random variables. This generality makes it possible to work with prior and posterior distributions on objects such as trees of unbounded depth and breadth, graphs, partitions, sets of monotone functions, sets of smooth functions and sets of general measures.

Applications of nonparametric Bayesian methods have begun to appear in disciplines such as information retrieval, natural language processing, machine vision, computational biology, cognitive science and signal processing. Because of their flexibility, they can also be used to express prior knowledge without restricting to small parametric classes. Furthermore, research on nonparametric Bayesian models has served to enhance the links between statistical machine learning and a number of other mathematical disciplines, including stochastic processes, algorithms, optimization, combinatorics and knowledge representation.

There have been several previous workshops on nonparametric Bayesian methods at machine learning conferences, including workshops at NIPS in 2003 and 2005 and a workshop at ICML workshop in 2006. This workshop aims to build on the success of these earlier workshops and to catalyze further research. There are many problem areas that need additional attention; these include (1) the development of new Monte Carlo and variational algorithms for inference; (2) the combination of ideas from knowledge representation and nonparametric Bayesian analysis to develop formal languages for specifying and manipulating flexible Bayesian models; (3) the problem of finding objective priors that work in the nonparametric Bayesian setting; (4) theoretical analysis of the conditions under which nonparametric Bayesian methods succeed or fail; and (5) the ongoing need to find compelling applications that serve to exhibit recent developments and to drive further research. This workshop is intended to bring together the growing community of nonparametric Bayesian researchers to explore these and other issues.

FORMAT:

The one-day workshop consists of three invited talks, three contributed talks, a round-table discussion on theory, methodology and applications, a round-table discussion on general-purpose language and software, a poster session, and a panel discussion.

CALL FOR PARTICIPATION:

Researchers interested in presenting their work and ideas at the workshop should send an email to moc.liamelgoog|seyabpn#moc.liamelgoog|seyabpn with the following information:

Title

Authors

Abstract (maximum 2 pages, ICML style pdf)

Preferred contribution (talk, poster, or round-table participation)

We expect authors to provide a final version of their papers by late June for inclusion on the workshop home page. Papers chosen for contributed talks shall also be expected to liaise with a discussion leader who will be in charge of stimulating discussion of the work at the workshop.